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Weakly-Supervised Semantic Segmentation

The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.

( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )

Papers

Showing 4150 of 296 papers

TitleStatusHype
Finding Meaning in Points: Weakly Supervised Semantic Segmentation for Event CamerasCode1
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic SegmentationCode1
Discriminative Region Suppression for Weakly-Supervised Semantic SegmentationCode1
Context Decoupling Augmentation for Weakly Supervised Semantic SegmentationCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak SupervisionCode1
Class Re-Activation Maps for Weakly-Supervised Semantic SegmentationCode1
Class Tokens Infusion for Weakly Supervised Semantic SegmentationCode1
Hierarchical Semantic Contrast for Weakly Supervised Semantic SegmentationCode1
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
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